YoloV8 improvement strategy: Diverse Branch Block improves YoloV8 and continues to fight against wolves on the heavy parameter structure. - Code World

YoloV8 improvement strategy: Diverse Branch Block improves YoloV8 and continues to fight against wolves on the heavy parameter structure.

Enterprise 2023-09-29 20:49:33 views: null

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Origin blog.csdn.net/m0_47867638/article/details/132839388

YoloV8 improvement strategy: Diverse Branch Block improves YoloV8 and continues to fight against wolves on the heavy parameter structure.

YoloV8 improvement strategy: RepViT improves YoloV8, lightweight Block helps YoloV8 achieve better mobility

YoloV8 improvement strategy: FastVit and YoloV8 are perfectly integrated, and the YoloV8 network is reconstructed with heavy parameters (first release on the entire network)

YoloV8 improvement strategy: Intel parameter-oriented efficient dynamic convolution KernelWarehouse, YoloV8’s top-notch package

YoloV8 improvement strategy: Based on hierarchical attention FasterViT, let YoloV8 achieve a leap in performance

YoloV8 improvement strategy: lightweight Slim Neck creates the ultimate YoloV8

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: CoordConv adds coordinates to the convolution, making it spatially aware.

YoloV8 improvement strategy: Shape-IoU, considering the measurement of border shape and scale

YoloV8 improvement strategy: deformable large-core attention D-LKA, YoloV8’s super-large sauce latte

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.72] attention mechanism Linear Context Transform Block (LCT module)

YoloV8 improvement strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

YOLOV5/YOLOV7/YOLOV8 Improvement Column

Evaluate the parameter amount and speed of the modified YOLOv8 model

Yolov8 improves the CoTAttention attention mechanism, and the effect instantly kills CBAM and SE

YOLOv8 explanation

yolov8 use

Реализовать обучение пользовательским данным на основе YOLOv8 — обнаружение дефектов печатных плат

Improvements to YOLOv8

yolov8 training

[YOLOv7/YOLOv5 series improvement NO.52] Integrating the C2f module in YOLOv8

yolov3 yolov4 yolov5 yolov6 yolov7 yolov8 yolox Algorithm improvement, algorithm customization, algorithm guidance

Improve YOLOv8 | Feature Fusion | YOLOv8 Application BiFPN Structure | "EfficientDet: Scalable and Efficient Target Detection"

Yolov8 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

Yolov8 improvement --- attention mechanism: CoordAttention, the effect spikes CBAM, SE

YoloV8 improvement --- attention mechanism: the introduction of GCNet, GENet, the effect is better than SENet

YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points

YOLOV8 improvement: Add RCS-OSA module to improve detection speed

[Lightweight] Practical improvement of YOLOv8 | Replacing the backbone network Backbone with GhostnetV2

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